TAPAS.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RohithPRK/first-streamlit_app/blob/main/TAPAS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2999275a-0274-4084-dd93-6f841e649165"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m40.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m48.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m54.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "LxvdFGwcMaR5",
        "outputId": "1c5f6372-96fc-40ef-d786-1f03166ff361"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.1+cu118'"
            ],
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              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import torch\n",
        "torch.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "h9sUtADwMwR7",
        "outputId": "e54b571c-2980-4354-cbd7-3cfe09a24bde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.0.1+cu118.html\n",
            "Collecting torch-scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_scatter-2.1.2%2Bpt20cu118-cp310-cp310-linux_x86_64.whl (10.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.2/10.2 MB\u001b[0m \u001b[31m70.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.1.2+pt20cu118\n"
          ]
        }
      ],
      "source": [
        "!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-2.0.1+cu118.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "i_kpjBGWOsE_"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "import pandas as pd"
      ]
    },
    {
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      "outputs": [
        {
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            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.66k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/443M [00:00<?, ?B/s]"
            ],
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          "data": {
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/490 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/262k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "962fa41ca8d542e88e82e764d632422f"
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/154 [00:00<?, ?B/s]"
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              "model_id": "711a62a64b2243ce9a68c8c998b8e932"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "tqa = pipeline(task=\"table-question-answering\",model=\"google/tapas-base-finetuned-wtq\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "kk8R15o5OyiN"
      },
      "outputs": [],
      "source": [
        "table=pd.read_csv(\"/content/Urovant - Tableau (1).csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "_i9o0zErPMKp"
      },
      "outputs": [],
      "source": [
        "table = table.astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
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        },
        "id": "SZ1KAV11POOt",
        "outputId": "01bee89c-9ffc-468e-d4f8-23a6889919d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "        FLAG         TRXEQ         NRXEQ PDRP\n",
              "0  UNALIGNED   3942942.524   1750552.614   NO\n",
              "1        SUN     21965.444      11548.62   NO\n",
              "2        URO   35701784.77  15597557.432   NO\n",
              "3        LTC  58038310.796  29339881.408   NO\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "    </div>\n",
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          },
          "metadata": {},
          "execution_count": 34
        }
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            "LTC, URO\n"
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        "query = \"Give me the flag names with PDRP 'YES'\"\n",
        "print(tqa(table=table,query=query)[\"answer\"])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"list the trxeq and nrxeq values\"\n",
        "print(tqa(table=table,query=query)[\"answer\"])\n"
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      },
      "execution_count": 36,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "SUM > 3942942.524, 21965.444, 35701784.77, 58038310.796, 2132598.7, 1711059.014\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"what is the flag for smallest NRXEQ\"\n",
        "print(tqa(table=table,query=query)[\"answer\"])\n"
      ],
      "metadata": {
        "colab": {
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        },
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      },
      "execution_count": 44,
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          "text": [
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          ]
        }
      ]
    }
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